中国石油长庆油田：井下作业“智变”带来“质变”

Core Viewpoint - The introduction of an intelligent downhole operation system at the Changqing Oilfield is set to revolutionize shale oil development by significantly enhancing operational efficiency and decision-making speed, addressing challenges in the industry [3][4]. Group 1: Technological Advancements - The intelligent system, referred to as the "shale oil brain," allows for historical data and fault diagnosis to be accessed in under 10 seconds, a drastic reduction from over 40 minutes previously [3]. - The system aims to achieve zero time in decision response, zero delays in collaboration, zero blind spots in fault handling, and zero deviations in evaluation systems [3]. - The accuracy of fault identification has reached 90%, showcasing the system's effectiveness in improving operational efficiency [3]. Group 2: Industry Context - Shale oil is a crucial component of unconventional oil and gas resources, with its efficient development being vital for national energy security [3]. - The Changqing Oilfield accounts for over 50% of China's total shale oil production, yet faces challenges such as low operational efficiency and insufficient precise decision-making due to various factors [3][4]. Group 3: Innovation and Collaboration - The company emphasizes the need for innovative thinking to optimize industrial structure and leverage digital technologies for business model reconstruction and management transformation [4]. - Collaboration with specialized digital companies has led to the development of a diagnostic expert knowledge base, enhancing the entire operational process from perception to evaluation [4]. Group 4: Operational Efficiency - The intelligent system integrates over 20 parameters, including downhole pressure, temperature, and flow, enabling real-time monitoring and preemptive alerts for operational issues [4][6]. - The transition from "post-repair analysis" to "pre-repair warning" has significantly reduced response times for downhole diagnostics from hours to seconds [4]. - The application of the intelligent model is expected to lower maintenance costs and improve decision-making efficiency, with average fault handling times previously exceeding three days [6].